Systems, methods, and devices for automatic signal detection based on power distribution by frequency over time within a spectrum

• US 10,459,020 B2
• Filed: 11/05/2018
• Issued: 10/29/2019
• Est. Priority Date: 01/23/2017
• Status: Active Grant

• Current Assignee: Digital Global Systems, Inc.
• Original Assignee: DGS Global Systems, Inc.
• Inventors: Dzierwa, Ronald C., Kleinbeck, David William
• Primary Examiner(s): Liao, Hsinchun
• Application Number:
  US16/180,690
• Publication Number:
  US 20190072601A1
• Time in Patent Office: 358 Days
• CPC Class Codes: G01R 29/0892 Details related to signal analysis or treatment; presenting results, e.g. displays; measuring specific signal features other than field strength, e.g. polarisation, field modes, phase, envelope, maximum value
• CPC Class Codes: G08B 21/18 Status alarms G08B21/02 takes precedence
• CPC Class Codes: G08B 29/185 Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system

Abstract

Systems, methods and apparatus for automatic alarm management in a radio-frequency (RF) environment are disclosed. An apparatus calculates a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of FFT data of the RF environment. The apparatus then creates a baseline based on the power distribution by frequency of the RF environment in a period of time, identifies at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution in real time or near real time to the baseline of the RF environment, identifies at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation, and sends at least one alarm comprising details of the at least one signal identified in the at least one alarm situation.

Claims

1. A method for automatic alarm management in a radio-frequency (RF) environment, comprising:

learning the RF environment for a period of time based on statistical learning techniques, including power level measurements of the RF environment;

creating a baseline based on the power level measurements of the RF environment for the period of time;

calculating a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of fast Fourier transform (FFT) data of the RF environment;

identifying at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution to the baseline of the RF environment;

identifying at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation; and

sending at least one alarm comprising data relating to the at least one signal identified in the at least one alarm situation.

2. The method of claim 1, wherein the baseline is an FFT array with an average of maximum power values for the period of time.

3. The method of claim 1, wherein the power distribution by frequency of the RF environment includes a 2-dimensional (2D) array with frequency as a horizontal axis and power as a vertical axis over a set length of time.

4. The method of claim 3, wherein the set length of time is adjustable.

5. The method of claim 3, wherein the power distribution by frequency of the RF environment includes a 3-dimensional (3D) array describing how often the power and the frequency is populated in the 2D array.

6. The method of claim 1, further comprising determining a power, a frequency, or a bandwidth of the at least one signal based on the first derivative of FFT data in the at least one alarm situation.

7. The method of claim 1, further comprising determining the at least one signal varies over time based on the second derivative of FFT data in the at least one alarm situation.

8. The method of claim 1, wherein the multiplicity of alarm triggering conditions comprise an alarm duration, a db offset, and a count, wherein the alarm duration is a time window a signal needs to appear to be considered as an alarm, wherein the db offset is the db value the signal needs to be above the baseline to be considered as a potential alarm, and wherein the count is the number of times the alarm duration and the db offset need to happen before an alarm is triggered.

9. The method of claim 1, wherein the at least one alarm is sent via text or email.

10. The method of claim 1, wherein the at least one signal is a narrowband signal hidden in a wideband signal.

11. The method of claim 1, wherein the at least one signal includes a signal with repetitive millisecond durations.

12. An apparatus for automatic alarm management in a radio-frequency (RF) environment, comprising:

at least one RF receiver, a generator engine, and an analyzer engine;

wherein the at least one RF receiver is configured to receive RF data and generate fast Fourier transform (FFT) data based on the RF data;

wherein the generator engine is configured to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of FFT data of the RF environment; and

wherein the analyzer engine is configured to create a baseline based on the power distribution by frequency of the RF environment in a period of time, identify at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution in real time or near real time to the baseline of the RF environment, identify at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation, and send at least one alarm comprising data relating to the at least one signal identified in the at least one alarm situation.

13. The apparatus of claim 12, further comprising a graphical user interface operable to configure, monitor, and manage job functions of the apparatus.

14. The apparatus of claim 12, wherein the analyzer engine is further operable to obtain signal detection parameters based on machine learning.

15. The apparatus of claim 12, wherein the apparatus is vehicle mounted.

16. The apparatus of claim 12, wherein the apparatus is transportable.

17. The apparatus of claim 12, wherein the apparatus is man-portable.

18. A system for automatic alarm management in a radio-frequency (RF) environment, comprising:

at least one apparatus at an edge of a network;

wherein the at least one apparatus comprises at least one RF receiver, a generator engine, and an analyzer engine;

wherein the at least one RF receiver is configured to receive RF data and generate fast Fourier transform (FFT) data based on the RF data;

wherein the generator engine is configured to calculate a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of the FFT data; and

wherein the analyzer engine is configured to create a baseline based on the power distribution by frequency of the RF environment in a period of time, identify at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution in real time or near real time to the baseline of the RF environment, identify at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation, and send at least one alarm comprising details of the at least one signal identified in the at least one alarm situation.

19. The system of claim 18, wherein the at least one apparatus is operable to identify mobile phones from 800 MHz to 2600 MHz.

20. The system of claim 18, wherein the at least one apparatus is operable to identify a drone with location information, direction information, an operator name, a drone transmission type, and a Media Access Control (MAC) address.